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spoorendonk on master
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spoorendonk on master
knapsack less output num customers from arg and 5 more (compare)
spoorendonk on use-knapsack
update knapsack value (compare)
spoorendonk on use-knapsack
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spoorendonk on use-knapsack
knapsack less output num customers from arg and 2 more (compare)
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The first model seems fine. Though it is very hard to write the transit time constraints for the edge formulation. I'm also a bit curious to whether limiting the flow on each arc for a commodity to be integer, is strictly identical to limiting the flow on each path to be integer. It is easy to find a counter-example, but I could be that they have the same space of optimal solutions
what counts as an integer variable in the subproblem? For us the subproblems are RCSPPs, so they are always integer, no? but that doesn't meant that lambda naturally becomes integer, right?
in relation to the above. If edge flow variables are integers in can ultimately result in integer path variables.
type="I"
is maybe not super clear
I also added the strong constraints to the model. The next step would be to se if we can add them dynamically with some sort of callback =)
working on it, got could up in some multi threading for solving subproblems in parallel
but ok, yeah, whenever I could be of any help, you just say so =)
I don't know how much time you have on your hands? And what kind of help you would find interesting?
Setting up models to expose stuff that does/doesn't work helps me a lot - like the ttfcmcf. In that direction I am locking into https://github.com/GregorCH/ipet to thoroughly test and track running times.
It could also go deeper into the c++ if you feel really confident!
in digging in my well documented codebase...
def callback(cb: CallbackModel, where: Where):
if where == Where.PathMipCuts:
relax = cb.relaxation
for y in y_vars:
e = (arcs[y.id].start, arcs[y.id].end)
xEdges = [x for k in range(k) for x in x_vars[k] if x.edge = e]
xksum = sum([relax[x.id] for x in xEdges])
if xksum > relax[y.id]:
cb.addCut(xsum([ 1*x for x in xEdges]) <= y)
tt_r18.1_12.csv
objval: 372254.0000000172
real 1m14.640s
user 15m30.381s
sys 0m10.323s
with and without cuts in 06 example
Alps0208I Search completed.
Alps0261I Best solution found had quality 250351 and was found at depth 32
Alps0265I Number of nodes fully processed: 20
Alps0266I Number of nodes partially processed: 15
Alps0267I Number of nodes branched: 17
Alps0268I Number of nodes pruned before processing: 0
Alps0270I Number of nodes left: 0
Alps0272I Tree depth: 7
Alps0274I Search CPU time: 143.07 seconds
Alps0278I Search wall-clock time: 83.62 seconds
================ DECOMP Statistics [BEGIN]: ===============
Total Decomp = 83.60 100.00 35 3.51
Total Solve Relax = 0.00 0.00 0 0.00
Total Solve Relax App = 0.00 0.00 0 0.00
Total Solution Update = 0.79 0.94 109 0.05
Total Generate Cuts = 72.43 86.63 48 1.59
Total Generate Vars = 6.59 7.88 82 0.10
Total Compress Cols = 0.04 0.05 14 0.01
================ DECOMP Statistics [END ]: ===============
Node 32 process stopping on bound. This LB= 250366 Global UB= 250351.
Alps0208I Search completed.
Alps0261I Best solution found had quality 250351 and was found at depth 30
Alps0265I Number of nodes fully processed: 18
Alps0266I Number of nodes partially processed: 15
Alps0267I Number of nodes branched: 16
Alps0268I Number of nodes pruned before processing: 0
Alps0270I Number of nodes left: 0
Alps0272I Tree depth: 7
Alps0274I Search CPU time: 48.34 seconds
Alps0278I Search wall-clock time: 3.56 seconds
================ DECOMP Statistics [BEGIN]: ===============
Total Decomp = 3.54 100.00 33 0.31
Total Solve Relax = 0.00 0.00 0 0.00
Total Solve Relax App = 0.00 0.00 0 0.00
Total Solution Update = 0.86 24.17 134 0.05
Total Generate Cuts = 0.00 0.00 49 0.00
Total Generate Vars = 0.66 18.52 97 0.01
Total Compress Cols = 0.06 1.70 21 0.00
================ DECOMP Statistics [END ]: ===============